Artificial Intelligence Full Course | Artificial Intelligence Tutorial for Beginners | Edureka

This comprehensive AI course covers a wide range of topics, starting with the history and applications of AI, then diving into machine learning fundamentals. Key ML concepts include supervised learning algorithms like linear and logistic regression, decision trees, random forests, Naive Bayes, KNN, and SVM. Unsupervised learning is explored through K-means clustering. Reinforcement learning is also covered, with practical demos illustrating each technique.

The course transitions into deep learning, explaining how neural networks work, including single and multi-layer perceptrons, backpropagation, and different network architectures like recurrent and convolutional neural networks. Natural Language Processing (NLP) is introduced, covering text mining, key NLP terminologies, and practical applications. The course also contrasts AI, ML, and deep learning, highlighting the limitations of ML and the advancements offered by deep learning.

Technologies and tools covered include Python, TensorFlow, and Keras, essential for implementing AI and ML solutions. The curriculum also touches upon advanced topics such as Bayesian and Markov models, inference, decision-making, bandit algorithms, the Bellman equation, and policy gradient methods, providing a solid foundation for developers and engineers looking to build intelligent systems.